The currently ongoing climate change and the debate about possible measures to be taken to limit the consequences of climate change, requires to know and understand the future response of the climate system to greenhouse gas emissions. Classical measures of climate change such as the Equilibrium Climate Sensitivity (ECS) are inherently linear and unable to account for abrupt transitions due to (interacting) tipping elements.

In this presentation I will discuss more general notions of climate sensitivity defined on a climate attractor that can be useful in understanding the response of a climate state to changes in radiative forcing. For example, a climate state close to a tipping point will have a degenerate linear response to perturbations, which can be associated with extreme values of the ECS. While many identified tipping elements in the climate system are regional and may have no direct impact on the global mean temperature, cascades of tipping elements can potentially have an impact, initiated by the threshold of the leading tipping element in a cascade.

How to cite: von der Heydt, A.: Dynamical systems approaches to climate response and climate tipping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3204, https://doi.org/10.5194/egusphere-egu23-3204, 2023.

Climatological risk assessments are currently based on simulations for the twenty-first century made using global climate models (GCMs) from the sixth Coupled Model Intercomparison Project Phases (CMIP6) by adopting several hypothetical shared socioeconomic pathways (SSPs) emission scenarios. However, there are large differences in the climatic sensitivity to atmospheric CO2 increase amongst the available climate models: for example, for the CMIP6 GCM the ECS varies between 1.8°C and 5.7°C and the TCR varies from 1.2°C to 2.8°C. There is also mounting evidence that many GCMs are operating "too hot" and are therefore unreliable for informing climate change policy for the future. Here, we assess the performance of 41 CMIP6 GCMs by ranking and comparing them using their literature-provided estimates for the equilibrium climate sensitivity (ECS) and transient climate response (TCR). We discover that the GCM sub-ensemble that performs the best in hindcasting the warming from 1980 to 2021 is that made of the GCMs with ECS ranging between 1.8 and 3.0 °C and TCR ranging between 1.2 and 1.8 °C. A total of 17 models make up this GCM sub-ensemble. The predicted warming of these models for the mid-term (2041-2060) period is 1.5–2.5°C relative to the preindustrial period (1850-1900) according to various SSP scenarios. Thus, the global aggregated impact and risk assessments assuming low to no adaptation appear, therefore, moderate, which also implies that adaptation policies may be adequate to address any unfavorable effects of future climate changes. We also discuss the additional uncertainties surrounding the warming of the Earth's surface temperature by comparing several temperature reconstructions and the warming differences observed between land and ocean to discuss the possibility that ECS and TRC could be furtherly constrained.

Main references:

Scafetta N (2022) Advanced testing of low, medium, and high ECS CMIP6 GCM simulations versus ERA5-T2m. Geophys Res Lett 49:e2022GL097716. https://doi.org/10.1029/2022GL097716

Scafetta, N. CMIP6 GCM ensemble members versus global surface temperatures. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06493-w

How to cite: Scafetta, N.: Constraining ECS and TCR for 21st century for temperature forecasts and risk assessments by comparing the CMIP6 GCM simulations versus global surface temperature records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4493, https://doi.org/10.5194/egusphere-egu23-4493, 2023.

The Arctic is the fastest warming region on Earth. Understanding how a rapidly changing climate change impacts Arctic systems is therefore an important challenge. This is the basis of the `Compost-Bomb' instability, a theorized runaway heating of northern latitude peat soils when atmospheric temperature rises faster than some critical rate, first proposed in [Luke & Cox, European Journal of Soil Science (2011), 62.1] and analysed in [Wieczorek et al, Proceedings of the Royal Society A (2011), 467.2129]. The Compost Bomb instability was one of the first examples of what is known as Rate-induced tipping or R-tipping.

The key trigger for the compost bomb instability is heat produced by microbial respiration. Here, the original soil carbon and temperature model of Luke & Cox is augmented with a non-monotone microbial respiration function, for a more realistic representation of the process. This gives rise to a meta-stable state, reproducing the results of [Khvorostyanov et al, Tellus (2008), 60B] where a complex PDE model is used. Two non-autonomous climate forcings are examined: (i) a rise in mean air temperature over decades (ii) a short-lived extreme weather event, with the rate-induced compost bomb observed in each. Using techniques of compactification, singular perturbation theory and desingularisation, we reduce the R-tipping problem to one of heteroclinic orbits, uncovering the tipping mechanism for each climate change scenario.

How to cite: Mulchrone, K., O'Sullivan, E., and Wieczorek, S.: Rate-Induced Tipping of the Compost Bomb: Sizzling Summers, Heteroclinic Canards and Metastable Zombie Fires, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3905, https://doi.org/10.5194/egusphere-egu23-3905, 2023.

Low-order coupled models of the atmosphere and ocean can illuminate the role of weather-climate interactions in long-term climate prediction. An important example is models that bring together the interplay between the Atlantic meridional overturning circulation (AMOC) with mid-latitude quasi-geostrophic dynamics of the atmosphere, as in the coupled model introduced by Van Veen et al (2001). In such models, the AMOC can transition from its present thermally driven to a much weaker salinity-driven state, through a tipping point. We show using these coupled models that, for scenarios with intermediate forcing between a strong and weak circulation, the long-term evolution shows extreme sensitivity to initial conditions, due to the appearance of riddled basins of attraction. The literature on dynamical systems has extensively examined such dynamics when two distinct basins of attraction are riddled, that is any small part of one attractor’s basin also includes a piece of the other. Moreover, in the presence of feedback from the atmosphere to the ocean, initial atmospheric conditions are amplified to the extent that long-term prediction in these models is inhibited by the finite precision at which the atmospheric state is known. We propose to describe the various facets of this phenomenon and consider the lessons for understanding and predicting long-term climate (in our case, thermohaline circulation), given initial state uncertainty. Furthermore, the resulting challenges of long-term prediction are not necessarily ameliorated by the real-world asymmetries in the model. When the relevant symmetries that yield riddled basins are broken through perturbations to the vector fields, the asymptotic dynamics become perfectly predictable given the initial conditions; however, long-term uncertainties in the transient state (strong vs weak circulation) persist for centuries, owing to ocean timescales.

How to cite: Seshadri, A. K. and Stainforth, D.: Challenges of long-term AMOC prediction due to riddled basins in coupled atmosphere-ocean models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6055, https://doi.org/10.5194/egusphere-egu23-6055, 2023.

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). By constraining the predictor through observations, it is possible to narrow future model projections, if a significant correlation between the predictor and the predictand exists. In our work, the EC technique has been applied to the analysis of precipitation and precipitation extremes, variables that are strongly affected by model uncertainties and still insufficiently analyzed in the context of ECs. One of the main challenges in determining an EC is establishing if the relationship found is physically meaningful and if it is robust to changes in the composition of the model ensemble. Four ECs already documented in the literature and so far tested only with CMIP3 or CMIP5, have been reconsidered in our study. Their existence and robustness are evaluated by developing a systematic methodology that involves different subsets and different scenarios of CMIP5 and CMIP6 models, verifying if an EC found in CMIP3/CMIP5 is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Three out of the four ECs considered in our work did not pass the test, being robust in CMIP5 but not in CMIP6, or (in one case) being not robust in both CMIP5 and CMIP6. Only one EC is verified and robust in both model ensembles. These results show the difficulty of identifying robust precipitation ECs and cast doubts on the usability of such ECs as a tool to  reduce uncertainties in future projections of precipitation change. At the same time, this work highlights the importance of the EC technique  as a way to improve our understanding  of climate phenomena and their drivers and to investigate precipitation-related feedbacks, providing evidence of connections between precipitation and different climate variables. This observation lays the path to further explore original ECs.

How to cite: Ferguglia, O., Palazzi, E., and von Hardenberg, J.: Evaluation of Precipitation Emergent Constraints in CMIP5 and CMIP6, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13608, https://doi.org/10.5194/egusphere-egu23-13608, 2023.

Vegetation in semi-arid regions has adapted to low rainfall by organizing itself in patterns, which can be described by reaction-diffusion equations that include local positive feedback (e.g. infiltration) and non-local mitigation (e.g. lateral water flow). This allows the vegetation to survive in lower rainfall conditions and creates multiple stable states, meaning that for a fixed amount of rainfall, the vegetation can exist in different patterns. It is possible to identify these different equilibrium states and to create a bifurcation diagram for the model.
As the climate changes, there is likely to be a shift in the rainfall patterns in the Sahel, although it is not yet clear if there will be an increase or decrease in precipitation. In this context, we use the vegetation pattern model and its bifurcation diagram to understand how it will respond to slow and fast changes in rainfall, and explore the possibility of rate-induced tipping (R-tipping). 
On the other hand, rainfall, land use and fires have a stochastic component, which we represent by adding two types of noise to the system: homogeneous and heterogeneous. This noise can cause a switch from one stable equilibrium to another, known as N-tipping, depending on the type of noise applied. The vegetation pattern is more stable to homogeneous noise than heterogeneous, but when it tips the homogeneous noise kill all the vegetation.
Understanding how patterned systems respond to changing environments and noise is critical for predicting the future evolution of various patterned systems globally, not just vegetation in the Sahel.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant no. 820970). 

How to cite: Vanderveken, L., Martínez Montero, M., and Crucifix, M.: R-tipping and N-tipping in a vegetation pattern model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6484, https://doi.org/10.5194/egusphere-egu23-6484, 2023.

The North Atlantic is a pacemaker of global climate through the Atlantic meridional overturning circulation (AMOC) and a large anthropogenic carbon uptake. Removal of anthropogenic carbon in the atmosphere by the ocean is key mechanism for modulating warming rate of globe. But, there is a large uncertainty in climate models for simulating AMOC and anthropogenic carbon uptake. To reduce the uncertainties in anthropogenic carbon uptake and its associated Northern Hemisphere surface warming, here we apply an emergent constraint. Sea surface salinity is often used to represent ocean circulations through its strong relationship with ocean density. The results suggest that the present-day sea surface salinity in the North Atlantic subpolar region constrains the future warming of the Northern Hemisphere by modulating anthropogenic carbon uptake in the North Atlantic. Models that generate a present-day higher SSS in the North Atlantic subpolar region systematically tend to a greater uptake of anthropogenic carbon, resulting in a slower warming in the Northern Hemisphere.

How to cite: Park, I.-H. and Yeh, S.-W.: North Atlantic carbon uptake modulates warming rate of the future Northern Hemisphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10784, https://doi.org/10.5194/egusphere-egu23-10784, 2023.

We describe a new statistical method to narrow uncertainty on estimates of past and climate change. Our approach can be viewed as an adaptation of Kalman Filtering, or Kriging, for Climate Change. The definition of what we call "signal" and "noise" are different from those used in typical weather forecasting systems, but the formalism is pretty similar, and estimation of the "model error" and "observational error" covariance matrices play a central role.

This approach allows us to simultaneously constrain projections, metrics of sensitivity, and to assess human influence on the past climate (attribution). It provides a consistent picture of on-going changes, through merging model simulations and observations in a Bayesian fashion. Cross-validation suggests that our method produces robust results and is not overconfident.

Beyond GSAT results, I will focus on application of this method to narrow uncertainty on regional or local scale warming -- which is a step forward from the AR6. Even at the local scale, we find that observational constraints narrow uncertainty on future warming, and that local observations provide useful information. The case of France will be used as an illustrative example, then I'll describe local results worldwide and show how they constrain warming patterns. I will briefly browse other applications, including some related to the water cycle, and discuss implications of this work. 

How to cite: Ribes, A. and Qasmi, S.: A Kalman Filtering approach to reduce uncertainty on global and regional climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12523, https://doi.org/10.5194/egusphere-egu23-12523, 2023.

Some components of the Earth system could change their state abruptly in response to a warming atmosphere and associated changes in climate conditions. This possibility has been recognized as one of the greatest potential threats associated with anthropogenic climate change. Examples  include the Atlantic Meridional Overturning Circulation, the polar ice sheets, the Amazon rainforest, and possibly the tropical monsoon systems.  The empirical evidence for abrupt climate transitions comes from paleoclimate proxy records, but also in observational records, signs of stability loss for some of the major tipping elements have been suggested. Here we explain some of the key theoretical concepts suggesting that tipping events may happen under ongoing climate change and summarize the empirical evidence for stability loss in some Earth system components with focus on candidates for future abrupt transitions. We argue that the critical forcing levels and rates are subject to large uncertainties and hence difficult to prdict. Improvements will require combining information from paleoclimate records, simulations with a hierarchy of models, and from observation-based data.

How to cite: Boers, N.: Theoretical and observational evidence for climate tipping points, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4917, https://doi.org/10.5194/egusphere-egu23-4917, 2023.